Why data strategy is the road to becoming AI-ready
Data is the key to successful AI. As Gartner points out, there are specific considerations to ensure it meets the requirements of AI use cases. It also needs to be comprehensive, representative, and adequately documented. Without trusted, structured and governed data, AI tools can’t consistently deliver what your business expects.
For South African companies, where data maturity varies widely across industries and teams, becoming AI-ready means investing in a scalable, business-aligned data strategy. This article explores how to put that strategy into action, avoid common mistakes, and utilise the Microsoft ecosystem to lay the foundations for meaningful AI outcomes.
- Why AI needs strong data to deliver business results
- Key principles of a Data Strategy
- How to structure data for AI workloads
- What South African organisations get wrong about data transformation
- How to build a practical roadmap for AI data readiness
Why does AI start with data?
AI is not a magic machine that you can throw data into and expect to receive consistent, high-quality, valuable insights. This scenario might work in your first few attempts, but inevitably, data engineers and scientists will spend increasing time preparing the data for mature projects.
A strong data foundation ensures that AI outputs are not flawed, biased, or irrelevant; yet many companies focus first on the tool – Copilot, GPT, or an automation suite – rather than building a robust data foundation to enable those tools to succeed.
AI is only as good as the data it’s built on, and in South Africa, where data quality, completeness and integration can vary, the need for a clear, organisation-wide data strategy is essential.
What does a real-world data strategy look like?
A data strategy is designed to ensure business data assets support business ambitions, including revenue growth, cost reduction, risk mitigation, and enhanced customer experience. Its purpose is to direct funding and effort to where it matters most.
Considering AI workloads, the data strategy will guide standards for sourcing, preparing, modelling and governing data. This eliminates conflicting outputs and arguments over which numbers are correct, thereby enhancing trust in the results and accelerating decision-making. By defining clear ownership, policies, and controls, organisations can limit their exposure to privacy breaches, fraud, and regulatory penalties.
An intentional roadmap prevents duplicate tools and ballooning cloud bills by setting guardrails for architecture and business. This leads to accelerated innovation through reliable, easily discoverable data on a trusted platform, which enables teams to experiment with AI/ML, automation, and new digital products.
Mint’s five-phase data strategy framework helps companies move from vision to execution.
- Listen – stakeholder mapping and value hypothesis per domain.
- Assess – conduct a current state analysis and create a capability backlog.
- Apply – define data governance, management, and analytics strategy, and further backlog grooming.
- Execute – begin delivery through engineering and platform activation.
- Mature – continuously monitor learning and outcomes and apply refinement.
Our approach to defining a Data Strategy encourages continuous iteration, allowing your AI implementation and business strategy to evolve in response to your needs.
How should I structure my data for AI workloads?
The sooner you prepare for the downstream AI processes in your data lifecycle, the greater the benefits will be. It’s like giving elite runners ready-to-consume fuel that they can grab while running, instead of a bowl of rice or making them wait in line at a water tap. It saves effort and reduces time to value.
The strategy for AI-ready data preparation should focus on data diversity, timing, accuracy, security, discoverability, and machine readability.
The mature adoption of a modern, cloud-based data platform enables the structuring of data according to the data strategy endorsed by business leaders. This ensures high-quality and trusted data assets for AI to consume, protecting your models from bias, poor input, or compliance risk.
Why solution-first thinking leads to failure
A common mistake is prioritising AI tools over AI outcomes, and teams end up rushing to deploy solutions without clarity on business value or the data structure and operational efficiency required to support them. Taking a tool-first approach leads to misaligned expectations, poorly performing models and frustration. Data-first thinking, supported by strategy, governance, and engineering, is what separates success from experimentation.
How to build a Roadmap
Mint’s teams help you define your data strategy and implementation roadmap in alignment with Microsoft’s Well-Architected Framework principles, ensuring your cloud infrastructure, access policies, and integration workflows are all designed to scale securely while maintaining flexibility and agility.
Set your business on a data roadmap that supports both long-term AI investments and short-term initiatives by aligning your data landscape with your company’s strategic vision.
